GOODS SENT ABROAD FOR PROCESSING. IMPLICATIONS OF THE NEW TREATMENT OF GOODS FOR PROCESSING IN THE SUPPLY AND USE TABLES CHANGING THE TREATMENT OF ‘GOODS SENT ABROAD FOR PROCESSING’: THE PRACTICAL AND ANALYTICAL IMPACT ON THE CANADIAN SNA PRODUCTION ACCOU
Bibliographic record
Abstract
The Revision 1 of 1993 SNA recommends not attributing a change of ownership to goods exported for processing except under well-specified circumstances, a treatment consistent with BPM5. The paper examines this change of treatment from the vantage point of a country with a large international trade sector, where outsourcing is most likely present in both directions but difficult to measure, and where input-output tables serve both as a benchmarks to its GDP (in current and constant prices) and as the basis for widely-used analytical models, productivity measures and other structural indicators. The paper outlines the impact of the existing treatment on industry and trade statistics and how it affects the measures derived from them such as input-output models, multifactor productivity indices, and other structural indicators. Second, it presents a summary of changes that need to be implemented at both the data-collection level and statistical estimation stage. The paper also suggests some of the benefits and some of the drawbacks that can be expected for supply and use tables. Finally, the paper outlines how the new treatment impacts the analytical roles that are traditionally associated with input-output tables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".